Assessing the Accuracy of Non-Rigid Registration With and Without Ground Truth

Abstract:

We compare two methods for assessing the performance of groupwise
non-rigid registration algorithms. The first approach, which has
been described previously, utilizes a measure of overlap between
ground-truth anatomical labels. The second, which is new, exploits
the fact that, given a set of non-rigidly registered images, a
generative statistical model of appearance can be constructed. We
observe that the quality of this model depends on the quality of
the registration, and define measures of model specificity
and generalisation- based on comparing synthetic images
sampled from the model, with those in the original image set -
that can be used to assess model/registration quality. We show
that both approaches detect the loss of registration accuracy as
the alignment of a set of correctly registered MR images of the
brain is progressively perturbed. We compare the sensitivities of
the two approaches and show that, as well as requiring no ground
truth, specificity provides the most sensitive measure of
misregistration.